Abstract
This paper proposes a method to discriminate benign or malignant of cell image. Proposed method uses a CNN to extract features from the nuclei images detected from the original cell images and benign or malignant is automatically classified by two classes classification with SVM. This paper treats a cancer classification method using features obtained by using CNN from cell nuclei extracted from images of melanoma. The effectiveness of the proposed method has been confirmed by experiments from the viewpoint of cell classification of benign or malignant. It is shown that the difference between the number of cell nuclei diagnosed as normal and the number of cell nuclei diagnosed as cancer is clear in the benign and metastasis, while the difference in malignant is vague. Moreover, it is confirmed that N/C ratio of malignancy and metastasis was slightly higher than that of benign.
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Acknowledgements
This research is supported by JSPS Grant-in-Aid for Scientific Research (C) (17K00252) and Chubu University Grant.
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Iwahori, Y., Tsukada, Y., Iwamoto, T., Funahashi, K., Ueda, J., Bhuyan, M.K. (2020). Classification of Cell Nuclei Using CNN Features. In: Lee, R. (eds) Computer and Information Science. ICIS 2019. Studies in Computational Intelligence, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-030-25213-7_13
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DOI: https://doi.org/10.1007/978-3-030-25213-7_13
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